---
library_name: pytorch
license: other
tags:
- backbone
- android
pipeline_tag: video-classification

---

![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet_2plus1d/web-assets/model_demo.png)

# ResNet-2Plus1D: Optimized for Qualcomm Devices

ResNet (2+1)D Convolutions is a network which explicitly factorizes 3D convolution into two separate and successive operations, a 2D spatial convolution and a 1D temporal convolution. It used for video understanding applications.

This is based on the implementation of ResNet-2Plus1D found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/video/resnet.py).
This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/resnet_2plus1d) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).

Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device.

## Getting Started
There are two ways to deploy this model on your device:

### Option 1: Download Pre-Exported Models

Below are pre-exported model assets ready for deployment.

| Runtime | Precision | Chipset | SDK Versions | Download |
|---|---|---|---|---|
| ONNX | float | Universal | QAIRT 2.42, ONNX Runtime 1.24.3 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet_2plus1d/releases/v0.52.0/resnet_2plus1d-onnx-float.zip)
| ONNX | w8a8 | Universal | QAIRT 2.42, ONNX Runtime 1.24.3 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet_2plus1d/releases/v0.52.0/resnet_2plus1d-onnx-w8a8.zip)
| QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet_2plus1d/releases/v0.52.0/resnet_2plus1d-qnn_dlc-float.zip)
| QNN_DLC | w8a8 | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet_2plus1d/releases/v0.52.0/resnet_2plus1d-qnn_dlc-w8a8.zip)
| TFLITE | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet_2plus1d/releases/v0.52.0/resnet_2plus1d-tflite-float.zip)
| TFLITE | w8a8 | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet_2plus1d/releases/v0.52.0/resnet_2plus1d-tflite-w8a8.zip)

For more device-specific assets and performance metrics, visit **[ResNet-2Plus1D on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/resnet_2plus1d)**.


### Option 2: Export with Custom Configurations

Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/resnet_2plus1d) Python library to compile and export the model with your own:
- Custom weights (e.g., fine-tuned checkpoints)
- Custom input shapes
- Target device and runtime configurations

This option is ideal if you need to customize the model beyond the default configuration provided here.

See our repository for [ResNet-2Plus1D on GitHub](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/resnet_2plus1d) for usage instructions.

## Model Details

**Model Type:** Model_use_case.video_classification

**Model Stats:**
- Model checkpoint: Kinetics-400
- Input resolution: 112x112
- Number of parameters: 31.5M
- Model size (float): 120 MB
- Model size (w8a8): 30.8 MB

## Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|---|---|---|---|---|---|---
| ResNet-2Plus1D | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 5.523 ms | 2 - 215 MB | NPU
| ResNet-2Plus1D | ONNX | float | Snapdragon® X2 Elite | 6.149 ms | 60 - 60 MB | NPU
| ResNet-2Plus1D | ONNX | float | Snapdragon® X Elite | 12.164 ms | 60 - 60 MB | NPU
| ResNet-2Plus1D | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 9.048 ms | 0 - 290 MB | NPU
| ResNet-2Plus1D | ONNX | float | Qualcomm® QCS8550 (Proxy) | 11.987 ms | 2 - 5 MB | NPU
| ResNet-2Plus1D | ONNX | float | Qualcomm® QCS9075 | 21.351 ms | 2 - 7 MB | NPU
| ResNet-2Plus1D | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 7.219 ms | 0 - 209 MB | NPU
| ResNet-2Plus1D | ONNX | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 1.923 ms | 0 - 191 MB | NPU
| ResNet-2Plus1D | ONNX | w8a8 | Snapdragon® X2 Elite | 2.048 ms | 31 - 31 MB | NPU
| ResNet-2Plus1D | ONNX | w8a8 | Snapdragon® X Elite | 4.542 ms | 31 - 31 MB | NPU
| ResNet-2Plus1D | ONNX | w8a8 | Snapdragon® 8 Gen 3 Mobile | 3.263 ms | 0 - 228 MB | NPU
| ResNet-2Plus1D | ONNX | w8a8 | Qualcomm® QCS6490 | 324.446 ms | 97 - 127 MB | CPU
| ResNet-2Plus1D | ONNX | w8a8 | Qualcomm® QCS8550 (Proxy) | 4.322 ms | 0 - 5 MB | NPU
| ResNet-2Plus1D | ONNX | w8a8 | Qualcomm® QCS9075 | 4.112 ms | 1 - 3 MB | NPU
| ResNet-2Plus1D | ONNX | w8a8 | Qualcomm® QCM6690 | 302.049 ms | 100 - 108 MB | CPU
| ResNet-2Plus1D | ONNX | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 2.594 ms | 0 - 189 MB | NPU
| ResNet-2Plus1D | ONNX | w8a8 | Snapdragon® 7 Gen 4 Mobile | 263.662 ms | 68 - 76 MB | CPU
| ResNet-2Plus1D | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 5.641 ms | 2 - 227 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Snapdragon® X2 Elite | 6.693 ms | 2 - 2 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Snapdragon® X Elite | 12.887 ms | 2 - 2 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 9.229 ms | 0 - 291 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 81.889 ms | 1 - 214 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 12.624 ms | 2 - 5 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Qualcomm® SA8775P | 21.268 ms | 0 - 214 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Qualcomm® QCS9075 | 23.064 ms | 4 - 8 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 29.099 ms | 0 - 270 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Qualcomm® SA7255P | 81.889 ms | 1 - 214 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Qualcomm® SA8295P | 22.733 ms | 0 - 197 MB | NPU
| ResNet-2Plus1D | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 7.175 ms | 0 - 216 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 1.845 ms | 1 - 184 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Snapdragon® X2 Elite | 2.32 ms | 1 - 1 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Snapdragon® X Elite | 4.909 ms | 1 - 1 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Snapdragon® 8 Gen 3 Mobile | 3.351 ms | 0 - 221 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Qualcomm® QCS6490 | 19.543 ms | 3 - 5 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Qualcomm® QCS8275 (Proxy) | 13.405 ms | 1 - 182 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Qualcomm® QCS8550 (Proxy) | 4.616 ms | 0 - 10 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Qualcomm® SA8775P | 4.664 ms | 1 - 186 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Qualcomm® QCS9075 | 4.763 ms | 1 - 3 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Qualcomm® QCM6690 | 84.546 ms | 1 - 199 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Qualcomm® QCS8450 (Proxy) | 7.751 ms | 1 - 217 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Qualcomm® SA7255P | 13.405 ms | 1 - 182 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Qualcomm® SA8295P | 7.848 ms | 1 - 182 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 2.575 ms | 1 - 182 MB | NPU
| ResNet-2Plus1D | QNN_DLC | w8a8 | Snapdragon® 7 Gen 4 Mobile | 7.885 ms | 1 - 188 MB | NPU
| ResNet-2Plus1D | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 258.531 ms | 0 - 247 MB | NPU
| ResNet-2Plus1D | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 297.22 ms | 0 - 316 MB | NPU
| ResNet-2Plus1D | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 714.79 ms | 0 - 237 MB | NPU
| ResNet-2Plus1D | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 377.527 ms | 0 - 3 MB | NPU
| ResNet-2Plus1D | TFLITE | float | Qualcomm® SA8775P | 392.608 ms | 0 - 236 MB | NPU
| ResNet-2Plus1D | TFLITE | float | Qualcomm® QCS9075 | 388.273 ms | 0 - 66 MB | NPU
| ResNet-2Plus1D | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 428.651 ms | 0 - 301 MB | NPU
| ResNet-2Plus1D | TFLITE | float | Qualcomm® SA7255P | 714.79 ms | 0 - 237 MB | NPU
| ResNet-2Plus1D | TFLITE | float | Qualcomm® SA8295P | 452.308 ms | 0 - 227 MB | NPU
| ResNet-2Plus1D | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 262.417 ms | 0 - 231 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 643.194 ms | 0 - 455 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Snapdragon® 8 Gen 3 Mobile | 578.421 ms | 0 - 514 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Qualcomm® QCS6490 | 1786.683 ms | 267 - 428 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Qualcomm® QCS8275 (Proxy) | 1520.257 ms | 0 - 439 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Qualcomm® QCS8550 (Proxy) | 755.68 ms | 0 - 3 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Qualcomm® SA8775P | 798.925 ms | 0 - 439 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Qualcomm® QCS9075 | 572.04 ms | 0 - 65 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Qualcomm® QCM6690 | 1606.748 ms | 310 - 471 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Qualcomm® QCS8450 (Proxy) | 862.725 ms | 0 - 466 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Qualcomm® SA7255P | 1520.257 ms | 0 - 439 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Qualcomm® SA8295P | 869.513 ms | 0 - 435 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 508.65 ms | 0 - 546 MB | NPU
| ResNet-2Plus1D | TFLITE | w8a8 | Snapdragon® 7 Gen 4 Mobile | 1224.497 ms | 294 - 365 MB | NPU

## License
* The license for the original implementation of ResNet-2Plus1D can be found
  [here](https://github.com/pytorch/vision/blob/main/LICENSE).

## References
* [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://arxiv.org/abs/1711.11248)
* [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/video/resnet.py)

## Community
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).